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[General] 最新SOL-C01考古題 & SOL-C01熱門認證

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【General】 最新SOL-C01考古題 & SOL-C01熱門認證

Posted at yesterday 14:33      View：7 | Replies：1        Print      Only Author   [Copy Link] 1#
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Snowflake SOL-C01 考試大綱：
主題簡介
主題 1
  • Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.
主題 2
  • Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.
主題 3
  • Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.
主題 4
  • Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.

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最新的 SnowPro Advanced SOL-C01 免費考試真題 (Q187-Q192):問題 #187
You have a Python script running in a Snowflake Notebook that retrieves data from a Snowflake table, performs some complex calculations, and then visualizes the results using Matplotlib. The script is running slowly, even after optimizing the SQL query. Which of the following steps would MOST likely improve the performance of the Python script within the Snowflake Notebook environment?
  • A. Store the intermediate results in a Snowflake temporary table and retrieve them later.
  • B. Use a smaller data sample by adding 'LIMIT 1 00' in the SQL query to speed up the process.
  • C. Vectorize the calculations using NumPy instead of looping through the data row by row.
  • D. Use the '%%osql' magic command to execute the calculations directly in Snowflake SQL.
  • E. Increase the size of the virtual warehouse associated with the Snowflake session.
答案：C
解題說明：
Option B, vectorizing calculations using NumPy, is the most likely to improve performance.
Python loops are generally slower than vectorized operations performed by NumPy. Vectorization allows NumPy to perform calculations on entire arrays at once, significantly speeding up the process. Increasing the warehouse size (A) primarily improves SQL query performance, not Python code execution. Using (C) would offload the calculation to SQL, which might be faster if the calculation can be expressed efficiently in SQL, but the question states that the SQL query has already been optimized. Storing intermediate results in a temporary table (D) might be helpful in some cases, but doesn't directly address the slow Python calculations. Option E reduces the data to improve performance, but it doesn't solve the underlying issue of slow python calculation.

問題 #188
Consider the following Snowflake SQL code snippet that attempts to load data from a CSV file into a table. Assume the file format 'MY CSV FORMAT' is correctly defined with appropriate delimiters and skip header settings.

Despite setting 'ON ERROR = 'CONTINUE", the COPY INTO operation fails and throws an error.
Which of the following scenarios could explain this behavior?
  • A. `ON_ERROR = 'CONTINUE" will always ensure a full load of the file and its impossible to find out reason
  • B. The user executing the COPY INTO command does not have the 'USAGE' privilege on the stage
    @MY STAGE'.
  • C. The file 'data.csv' is not present in the stage @MY_STAGE, and Snowflake cannot find the file to load.
  • D. The error is related to an integrity constraint violation (e.g., a unique key violation), and =
    'CONTINUE" does not handle integrity constraint violations.
  • E. The error is a parsing error related to data type mismatch, and 'ON ERROR = 'CONTINUE" only handles file-level errors, not row-level errors.
答案：B,C,D
解題說明：
Options B, C and D are correct. ERROR = 'CONTINUE'` primarily handles file-level errors (like a corrupted file that can't be opened) and certain row-level errors (like incorrect number of columns). It does not handle integrity constraint violations; these will always cause the COPY INTO to fail, even with `ON ERROR = 'CONTINUE". If the file is not present (Option C) or the user lacks the USAGE privilege (Option D), the COPY INTO will fail before it even attempts to parse the data, and 'ON_ERROR will not apply because Snowflake cannot even access the file.
Option A is incorrect as its not just parse level error, it may happen due to data validation contraints also. Option E is incorrect because 'ON_ERROR = 'CONTINUE'` will not ensure that the file is loaded no matter what, some issues will occur causing the COPY INTO to fail.

問題 #189
What does "warehouse scaling up/down" refer to in Snowflake?
  • A. Moving data between different storage locations
  • B. Changing the region of the warehouse
  • C. Changing the size of the warehouse (e.g., from Small to Medium or Vice Versa).
  • D. Adjusting the number of clusters in a multi-cluster warehouse.
答案：C
解題說明：
Scalingup or downrefers tovertical scaling, meaning the warehouse's compute size is increased or decreased.
For example, moving fromSmall # Medium # Largeincreases CPU, memory, and I/O capacity, enabling faster processing for compute-intensive workloads.
Vertical scaling improves single-query performance, large ETL jobs, complex joins, or transformations. It does not improve concurrency unless multi-cluster mode is also used.
Horizontal scaling (scaling out/in), by contrast, adjusts thenumber of clustersand is used for concurrency.
Region selection is fixed at account creation and cannot be changed by resizing a warehouse. Storage movement is unrelated to compute rescaling.

問題 #190
In the Query Profile, what does the Pruning section provide?
  • A. Information on how Snowflake removed objects from the query plan.
  • B. Information on how Snowflake removed micro-partitions from the query scan.
  • C. Information on how Snowflake removed rows from the query results.
  • D. Information on how Snowflake removed columns from the query results.
答案：B
解題說明：
The Pruning section of the Snowsight Query Profile showshow Snowflake eliminated unnecessary micro- partitions from the scan phaseof the query. Snowflake stores data in micro-partitions and maintains metadata such as min/max values for each column within each partition. When a query includes filters (e.g., WHERE clauses), Snowflake evaluates this metadata to determine which micro-partitions cannot possibly satisfy the predicate. These partitions are skipped, meaning they are never scanned or read from storage.
This process drastically improves performance because Snowflake minimizes I/O, reduces compute usage, and shortens execution time. Partition pruning is especially impactful on large tables because only a fraction of the stored micro-partitions typically need to be accessed.
The Pruning section does not show removedrows-that happens during the filter step. It does not show removedcolumns-column pruning is handled separately by the optimizer. It also does not show removedobjectsfrom the plan. Its sole purpose is to document micro-partition elimination and scan reduction.

問題 #191
A data scientist needs to create a temporary table in Snowflake to perform some data analysis.
The table should only be accessible within their current session and should be automatically dropped at the end of the session. Which of the following SQL statements is the CORRECT way to create such a table?
  • A. CREATE LOCAL TEMPORARY TABLE AS SELECT FROM existing_table;
  • B. CREATE TEMP TABLE AS SELECT FROM existing_table;
  • C. CREATE GLOBAL TEMPORARY TABLE AS SELECT FROM existing_table;
  • D. CREATE TABLE AS SELECT FROM existing_table;
  • E. CREATE VOLATILE TABLE AS SELECT FROM existing_table;
答案：B
解題說明：
The 'CREATE TEMP TABLE statement is the correct way to create a temporary table in Snowflake that is only visible within the current session and is automatically dropped when the session ends. 'GLOBAL TEMPORARY TABLE' and 'LOCAL TEMPORARY TABLE' are not valid Snowflake syntax. 'CREATE TABLE without 'TEMP' creates a permanent table. 'VOLATILE applies to functions, not tables.

問題 #192
......
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